class_docstr: Calculates the distance between points in a city road grid.
  
  Description:
      This distance is calculated using the haversine formula, which
      takes into account the curvature of the Earth.
      If either input data contains `NaN`s, the calculated
      distance with be `NaN`.
      This calculation is also known as the Mahnattan distance.
  
  Args:
      unit (str): Determines the unit value to output. Could
          be miles or kilometers. Default is miles.
  
  Examples:
      >>> cityblock_distance = CityblockDistance()
      >>> DC = (38, -77)
      >>> Boston = (43, -71)
      >>> NYC = (40, -74)
      >>> distances_mi = cityblock_distance([DC, DC], [NYC, Boston])
      >>> np.round(distances_mi, 3).tolist()
      [301.519, 672.089]
  
      We can also change the units in which the distance is calculated.
  
      >>> cityblock_distance_kilometers = CityblockDistance(unit='kilometers')
      >>> distances_km = cityblock_distance_kilometers([DC, DC], [NYC, Boston])
      >>> np.round(distances_km, 3).tolist()
      [485.248, 1081.622]
code: def __init__(self, unit="miles"):
      if unit not in ["miles", "kilometers"]:
          raise ValueError("Invalid unit given")
      self.unit = unit
  
file_path: featuretools/featuretools/primitives/standard/transform/latlong/cityblock_distance.py
imports: import numpy
  import pandas
  from woodwork.column_schema import ColumnSchema
  from woodwork.logical_types import Double, LatLong
  from featuretools.primitives.base import TransformPrimitive
  from featuretools.primitives.standard.transform.latlong.utils import _haversine_calculate
label_desc: Use image node_id 1 to create a new CityblockDistance object from inherited base classes: TransformPrimitive with example: obj = CityblockDistance(unit)